Getting started with R

From the very basics

Dr. Carina Nigg & dr. Judith Bouman

Introduction

Course content

Introduction of R and RStudio

  • Understanding R and RStudio
  • Using basic functions
  • Writing a first script
  • Understanding packages
  • Importing data
  • Using basic functions on imported data

Analyzing your own data

  • Organizing your data
  • Loading your data into R
  • Providing an overview of your data
  • Inspecting missing data
  • Checking plausibility of your data

Why learn R?

  • Reproducibility of your results
  • Free software
  • A lot of resources available
  • Potentially useful in further career

Disclaimers

  • First time we are teaching this course
  • We expect no prior knowledge
  • Ask questions
  • Tell us if we are too slow/fast

Installing R and RStudio

Did you all manage to install both R and RStudio?

Difference R and RStudio

Opening RStudio

Rstudio windows

Simple calculator

1 + 1 
[1] 2

Using a script

  • Track code
  • Make changes
  • Repeat code (reproducible science)

Now, you can download, save and open the “follow_along_script_monday_morning.R”

Code and comments

“#” can be use to add text and comments

# Use "#" to add text and explanations to your code

Keyboard shortcuts

  • Run the current line or selection: Ctrl + Enter / Option + Enter
  • Run all code in the script: Ctrl + Alt + R / Command + shift + Enter
  • Interrupt running code: Esc
  • Comment/uncomment lines: Ctrl + Shift + C / Command + shift + C

Saving “objects”

a = 1 
b = 2
c = a + b

# can use = or "<-"

c <- a + b 

c
[1] 3

Why could this be helpful?

Example “objects”

data = c(1,13,2,1,43,53,1,2,34,54,2,4,6,23)

cutoff = 10 

data[data>cutoff]
[1] 13 43 53 34 54 23

Using “functions”

Using “functions”

sum(1, 2)
[1] 3
sum(a , b)
[1] 3
?sum

Getting help with functions

?sum

Vector

c(1,2,3)
[1] 1 2 3
c(a, b)
[1] 1 2

Vector

How to access an element in the vector

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

a_vector[4]
[1] 3.11

Array/matrix

matrix(data = c(1,2,3,4,5,6,7,8), nrow = 4 )
     [,1] [,2]
[1,]    1    5
[2,]    2    6
[3,]    3    7
[4,]    4    8

Array/matrix

How to access an element from a matrix

a_matrix = matrix(data = c(1,2,3,4,5,6,7,8), nrow = 4 )

a_matrix[3,2] # first row, then column
[1] 7

Let`s try! – Exercise 1

Can you calculate the following for “a_vector”?

  • Mean
  • Standard deviation
  • Maximal value
  • Minimal value
  • Length of the vector
a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

Take 15 minutes to try

Let`s try! – Exercise 1 - Solution

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

mean(a_vector)
[1] 4.02425
sd(a_vector)
[1] 1.811263
max(a_vector)
[1] 6.21
min(a_vector)
[1] 1.23
length(a_vector)
[1] 8

Let`s try! – Exercise 2

Can you figure out what you can do with the following functions?

  • seq()
  • rep()

Take 10 minutes

Let`s try! – Exercise 2 - Solution

seq(1,10)
 [1]  1  2  3  4  5  6  7  8  9 10
seq(1,10, by = 2)
[1] 1 3 5 7 9
rep(0,100)
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Classes and types

Numeric and character

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

class(a_vector)
[1] "numeric"
b_vector = c("something", "something else", "another thing", "completely differnt")

class(b_vector)
[1] "character"

Classes and types

Logical

c_vector = c(F, T, T,T, F, T, F, T)

class(c_vector)
[1] "logical"

Classes and types

factor

gender <- factor(c("Male", "Female", "Female", "Male"))

class(gender)
[1] "factor"

Combining different types of vectors

Lists

my_list <- list(name = "Alice", age = 25, scores = c(90, 85, 88))

print(my_list)
$name
[1] "Alice"

$age
[1] 25

$scores
[1] 90 85 88

Why do we care about classes and types?

Helpful for plotting –> we come back to this later

Dataframe

# Create a data frame
df <- data.frame(x = 1:3, y = c("a", "b", "c"))

# Printing
print(df)
  x y
1 1 a
2 2 b
3 3 c

Tibble

# Create a tibble
library(tibble)
tb <- tibble(x = 1:3, y = c("a", "b", "c"))

print(tb)
# A tibble: 3 × 2
      x y    
  <int> <chr>
1     1 a    
2     2 b    
3     3 c

Packages

Get access to specific set of functions

#install.packages("tidyverse")
library(tidyverse)

run “library()” every time you want to use any function from this package

Let’s try!

Get your data in R

Organize your data

project_dir

Organize your data

Choose your working directory

getwd() 
[1] "/Users/jb22m516/Documents/GitHub/getting_started_with_R/slides"
setwd("/Users/jb22m516/Documents/GitHub/getting_started_with_R/")

“Path” to file in R

A “path” tells R where it can find your data

Functions for loading data

#install.packages("utils")
library(utils)

#data = read.csv2()

Types of data in R

tibble / dataframe / matrix / array

Introduction to simple plots